The present disclosure relates generally to performance capture, and more specifically to real-time performance capture techniques with on-the-fly correctives.
Performance capture techniques generally rely on generic expressions or require extensive training sessions in order to capture expressions of a subject (e.g., facial expressions). Such generic expressions do not accurately reflect the nuances of the subject's expressions. In order to create subject-specific output, pre-captured expressions may be used to train a system for tracking the expressions of the subject. A subject may be required to attend a lengthy training session in order to capture specific expressions so that the system can be adequately trained to model the expressions of the subject. Furthermore, performance capture methods may need to interpolate expressions of the subject due to lack of a particular pre-processed expression being captured in the training session. These interpolated expressions oftentimes lack an accurate depiction of the subtle traits of the subject. As a result, subtle nuances of a subject's expressions are difficult to capture due to the inability of the training sessions to capture every possible expression of a user before tracking begins and also due to interpolation methods lacking the ability to provide accurate results.
Embodiments of the invention address these and other problems both individually and collectively.